1990—2023年新疆地表水体面积动态变化及其驱动因素

doi:10.13866/j.azr.2025.01.04

Abstract

Abstract:

Xinjiang features a unique mountain-oasis-desert ecological system, in which the surface water body plays a crucial role in maintaining the ecological balance and supporting regional socioeconomic development. This study used Landsat 5, 7, 8, and 9 satellite remote sensing images and a mixed index algorithm to estimate Xinjiang’s surface water area from 1990 to 2023 for analysis of its spatial patterns and changes over time. Geographic detector methods were used to identify the factors influencing changes in the surface water area. The findings revealed that between 1990 and 2023, the area of the permanent water body in Xinjiang increased by 36.25% (2466.20 km²), driven primarily by the mountain water body. Notably, the inland river basins of the Qiangtang Plateau expanded significantly by approximately two-thirds (1149.58 km²). The seasonal water bodies, mainly consisting of the oasis-desert water body, also rose by 181.90% (1924.84 km²), with the mainstream Tarim River nearly doubling in area (344.92 km²). Changes in mountain water bodies were largely influenced by climatic factors, with the snow water equivalent contributing the highest average rate (42.84%). In contrast, human activities had a more substantial impact on the oasis-desert water body, with population density and cultivated land exhibiting average contribution rates of 64.10% and 54.43%, respectively. This study provides a comprehensive analysis of the temporal and spatial changes in Xinjiang’s surface water body and their driving factors, thereby offering critical scientific insights for assessing water resource development potential and formulating effective water resource management strategies in the region.

Key words: surface water body, Landsat, mountain-oasis-desert, climate change, Xinjiang

ZOU Bin, ZOU Shan, YANG Yuhui. Dynamic changes and driving factors of surface water body in Xinjiang from 1990 to 2023[J].Arid Zone Research, 2025, 42(1): 40-50.

Figures/Tables 9

Fig. 1

Fig. 2

Tab. 1

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

References 46

[1]	Pekel J F, Cottam A, Gorelick N, et al. High-resolution mapping of global surface water and its long-term changes[J]. Nature, 2016, 540(7633): 418-422.
[2]	Hall J W, Grey D, Garrick D, et al. Coping with the curse of freshwater variability[J]. Science, 2014, 346(6208): 429-430. doi: 10.1126/science.1257890 pmid: 25342791
[3]	Vörösmarty C J, McIntyre P B, Gessner M O, et al. Global threats to human water security and river biodiversity[J]. Nature, 2010, 467(7315): 555-561.
[4]	姚俊强. 新疆空中水资源和地表水资源变化特征研究[J]. 干旱区研究, 2024, 41(2): 181-190. doi: 10.13866/j.azr.2024.02.01
	[Yao Junqiang. Change in atmospheric and surface water resource in Xinjiang[J]. Arid Zone Research, 2024, 41(2): 181-190. ] doi: 10.13866/j.azr.2024.02.01
[5]	Yao J, Chen Y, Guan X, et al. Recent climate and hydrological changes in a mountain-basin system in Xinjiang, China[J]. Earth-Science Reviews, 2022, 226: 103957.
[6]	Shen Y J, Shen Y, Guo Y, et al. Review of historical and projected future climatic and hydrological changes in mountainous semiarid Xinjiang (northwestern China), Central Asia[J]. CATENA, 2020, 187: 104343.
[7]	Lu H, Zhao R, Zhao L, et al. A contrarian growth: The spatiotemporal dynamics of open-surface water bodies on the northern slope of Kunlun Mountains[J]. Ecological Indicators, 2023, 157: 111249.
[8]	Zheng L, Xia Z, Xu J, et al. Exploring annual lake dynamics in Xinjiang (China): Spatiotemporal features and driving climate factors from 2000 to 2019[J]. Climatic Change, 2021, 166(3): 36.
[9]	邹珊, 吉力力·阿不都外力, 黄文静, 等. 塔里木河下游生态输水对地表水体面积变化的影响[J]. 干旱区地理, 2021, 44(3): 681-690. doi: 10.12118/j.issn.1000–6060.2021.03.10
	[Zou Shan, Jilili Abuduwaili, Huang Wenjing, et al. Effects of ecological water conveyance on changes of surface water area in the lower reaches of Tarim River[J]. Arid Land Geography, 2021, 44(3): 681-690. ] doi: 10.12118/j.issn.1000–6060.2021.03.10
[10]	Alsdorf D E, Rodríguez E, Lettenmaier D P. Measuring surface water from space[J]. Reviews of Geophysics, 2007, 45(2): 1-24.
[11]	Liu X, Hu G, Chen Y, et al. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform[J]. Remote Sensing of Environment, 2018, 209: 227-239.
[12]	Wang X, Xiao X, Zou Z, et al. Gainers and losers of surface and terrestrial water resources in China during 1989-2016[J]. Nature Communications, 2020, 11(1): 3471.
[13]	Huang C, Chen Y, Zhang S, et al. Detecting, extracting, and monitoring surface water from space using optical sensors: A review[J]. Reviews of Geophysics, 2018, 56(2): 333-360.
[14]	McFeeters S K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996, 17(7): 1425-1432.
[15]	Xu H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery[J]. International Journal of Remote Sensing, 2006, 27(14): 3025-3033.
[16]	Sun F, Sun W, Chen J, et al. Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery[J]. International Journal of Remote Sensing, 2012, 33(21): 6854-6875.
[17]	Gómez C, White J C, Wulder M A. Optical remotely sensed time series data for land cover classification: A review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 116: 55-72.
[18]	Khatami R, Mountrakis G, Stehman S V. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research[J]. Remote Sensing of Environment, 2016, 177: 89-100.
[19]	Chen Y, Li Z, Fan Y, et al. Progress and prospects of climate change impacts on hydrology in the arid region of Northwest China[J]. Environmental Research, 2015, 139: 11-19. doi: 10.1016/j.envres.2014.12.029 pmid: 25682220
[20]	Abatzoglou J T, Dobrowski S Z, Parks S A, et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015[J]. Scientific Data, 2018, 5(1): 170191.
[21]	Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth System Science Data, 2021, 13(8): 3907-3925. doi: 10.5194/essd-13-3907-2021
[22]	Tapley B D, Bettadpur S, Ries J C, et al. GRACE measurements of mass variability in the earth system[J]. Science, 2004, 305(5683): 503-505. doi: 10.1126/science.1099192 pmid: 15273390
[23]	Zou Z, Xiao X, Dong J, et al. Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016[J]. Proceedings of the National Academy of Sciences, 2018, 115(15): 3810-3815.
[24]	Huang W, Duan W, Nover D, et al. An integrated assessment of surface water dynamics in the Irtysh River Basin during 1990-2019 and exploratory factor analyses[J]. Journal of Hydrology, 2021, 593: 125905.
[25]	Huang W, Duan W, Chen Y. Unravelling lake water storage change in Central Asia: Rapid decrease in tail-end lakes and increasing risks to water supply[J]. Journal of Hydrology, 2022, 614: 128546.
[26]	Zhou H, Liu S, Hu S, et al. Retrieving dynamics of the surface water extent in the upper reach of Yellow River[J]. Science of The Total Environment, 2021, 800: 149348.
[27]	Zou Z, Dong J, Menarguez M A, et al. Continued decrease of open surface water body area in Oklahoma during 1984-2015[J]. Science of The Total Environment, 2017, 595: 451-460.
[28]	Chen J, Kang T, Yang S, et al. Open-Surface water bodies dynamics analysis in the Tarim River Basin (North-Western China), based on Google Earth Engine cloud platform[J]. Water, 2020, 12(10): 2822.
[29]	李崇巍, 王志慧, 汤秋鸿, 等. 1986—2019年黄河流域地表水体动态变化及其影响因素[J]. 地理学报, 2022, 77(5): 1153-1168. doi: 10.11821/dlxb202205008
	[Li Chongwei, Wang Zhihui, Tang Qiuhong, et al. Dynamics of surface water area in the Yellow River Basin and its influencing mechanism during 1986-2019 based on Google Earth Engine[J]. Acta Geographica Sinica, 2022, 77(5): 1153-1168. ] doi: 10.11821/dlxb202205008
[30]	Olofsson P, Foody G M, Herold M, et al. Good practices for estimating area and assessing accuracy of land change[J]. Remote Sensing of Environment, 2014, 148: 42-57.
[31]	Sen P K. Estimates of the regression coefficient based on Kendall’s Tau[J]. Journal of the American Statistical Association, 1968, 63(324): 1379-1389.
[32]	Hamed K H, Ramachandra Rao A. A modified Mann-Kendall trend test for autocorrelated data[J]. Journal of Hydrology, 1998, 204(1): 182-196.
[33]	Mann H B. Nonparametric Tests Against Trend[J]. Econometrica, 1945, 13(3): 245-259.
[34]	王劲峰, 徐成东. 地理探测器: 原理与展望[J]. 地理学报, 2017, 72(1): 116-134. doi: 10.11821/dlxb201701010
	[Wang Jinfeng, Xu Chengdong. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 2017, 72(1): 116-134. ] doi: 10.11821/dlxb201701010
[35]	李稚, 朱成刚, 汪家友, 等. 东昆仑库木库里盆地典型湖泊水量蒸发损失估算[J]. 干旱区地理, 2024, 47(8): 1263-1276. doi: 10.12118/j.issn.1000-6060.2024.166
	[Li Zhi, Zhu Chenggang, Wang Jiayou, et al. Estimation of evaporation loss from typical lakes in the Kumukuli Basin, East Kunlun Mountains[J]. Arid Land Geography, 2024, 47(8): 1263-1276. ] doi: 10.12118/j.issn.1000-6060.2024.166
[36]	Tao S, Zhang H, Feng Y, et al. Changes in China’s water resources in the early 21st century[J]. Frontiers in Ecology and the Environment, 2020, 18(4): 188-193.
[37]	Yao F, Livneh B, Rajagopalan B, et al. Satellites reveal widespread decline in global lake water storage[J]. Science, 2023, 380(6646): 743-749. doi: 10.1126/science.abo2812 pmid: 37200445
[38]	Yao J, Chen Y, Zhao Y, et al. Climatic and associated atmospheric water cycle changes over the Xinjiang, China[J]. Journal of Hydrology, 2020, 585: 124823.
[39]	Chen Y, Li B, Fan Y, et al. Hydrological and water cycle processes of inland river basins in the arid region of Northwest China[J]. Journal of Arid Land, 2019, 11(2): 161-179. doi: 10.1007/s40333-019-0050-5
[40]	吝静, 赵成义, 马晓飞, 等. 基于生态系统服务价值的塔里木河干流土地利用结构优化[J]. 干旱区研究, 2021, 38(4): 1140-1151. doi: 10.13866/j.azr.2021.04.26
	[Lin Jing, Zhao Chengyi, Ma Xiaofei, et al. Optimization of land use structure based on ecosystem service value in the mainstream of Tarim river[J]. Arid Zone Research, 2021, 38(4): 1140-1151. ] doi: 10.13866/j.azr.2021.04.26
[41]	陈亚宁, 陈亚鹏, 朱成刚, 等. 西北干旱荒漠区生态系统可持续管理理念与模式[J]. 生态学报, 2019, 39(20): 7410-7417.
	[Chen Yaning, Chen Yapeng, Zhu Chenggang, et al. The concept and mode of ecosystem sustainable management in arid desert are as in Northwest China[J]. Acta Ecologica Sinica, 2019, 39(20): 7410-7417. ]
[42]	Geng Q, Zhao Y, Sun S, et al. Spatio-temporal changes and its driving forces of irrigation water requirements for cotton in Xinjiang, China[J]. Agricultural Water Management, 2023, 280: 108218.
[43]	Chen P, Wang S, Liu Y, et al. Water availability in China’s oases decreased between 1987 and 2017[J]. Earth’s Future, 2023, 11(4): e2022EF003340.
[44]	Xia Q Q, Chen Y N, Zhang X Q, et al. Identifying reservoirs and estimating evaporation losses in a large arid inland basin in Northwestern China[J]. Remote Sensing, 2022, 14(5): 1105.
[45]	陈永金, 艾克热木·阿布拉, 张天举, 等. 塔里木河下游生态输水对地下水埋深变化的影响[J]. 干旱区地理, 2021, 44(3): 651-658. doi: 10.12118/j.issn.1000–6060.2021.03.07
	[Chen Yongjin, Aikeremu Abula, Zhang Tianju, et al. Effects of ecological water conveyance on groundwater depth in the lower reaches of Tarim River[J]. Arid Land Geography, 2021, 44(3): 651-658. ] doi: 10.12118/j.issn.1000–6060.2021.03.07
[46]	Wu Q, Lane C R, Li X, et al. Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine[J]. Remote Sensing of Environment, 2019, 228: 1-13.

Sentinel-2A 目视解译结果	提取结果		总计/个	用户精度
Sentinel-2A 目视解译结果	水体/个	非水体/个	总计/个	用户精度
水体	1098	47	1145	95.90%
非水体	68	6787	6855	99.01%
总计	1166	6834	8000	总体精度=98.57%
生产者精度	94.17%	99.31%		Kappa系数=94.18%

Dynamic changes and driving factors of surface water body in Xinjiang from 1990 to 2023

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 46

Related Articles 15

Metrics

Comments

Recommended 0

[1]	LYU Ning, GUO Yu, PENG Qin, YIN Feihu, ZHANG Jiaqi, LIU Xingren, ZENG Mei, XU Zihan. Spatiotemporal evolution characteristics and contributing factors of the carbon effect in cultivated land use in Xinjiang [J]. Arid Zone Research, 2025, 42(1): 179-190.
[2]	SUN Linlin, LIU Qiong, HUANG Guan, CHEN Yonghang, WEI Xin, GUO Yulin, ZHANG Taixi, GAO Tianyi, XU Yunhong. Analysis of surface solar radiation under different cloud conditions in Xinjiang and the surrounding “Belt and Road” regions [J]. Arid Zone Research, 2024, 41(9): 1480-1490.
[3]	LYU Zhuangzhuang, QIAO Qingqing, DONG Sunyi, WANG Dong. Paleoclimatic evolution and driving mechanisms in arid areas of inland Asia during the Middle Miocene Climatic Optimum in the context of global climate warming [J]. Arid Zone Research, 2024, 41(8): 1309-1322.
[4]	ZHOU Jie, WANG Xuhu, DU Weibo, ZHOU Xiaolei, YANG Jie, ZAHNG Xiaowei. Prediction of potential distribution area of Picea schrenkiana under the background of climate change [J]. Arid Zone Research, 2024, 41(7): 1167-1176.
[5]	JIAN Zhengbo, LUO Hao, SHAN Nana. A study on the spatial and temporal evolution and carbon effects of production-living-ecological in Xinjiang under carbon peak and carbon neutrality goals [J]. Arid Zone Research, 2024, 41(7): 1238-1248.
[6]	LIU Huaqing, WANG Bo, JIA Yanyan, XIE Xinran, ZHANG Wei. Characterization of the freezing injury to Juglans regia at different slope positions in the West Tianshan valley of Xinjiang, China [J]. Arid Zone Research, 2024, 41(6): 1079-1088.
[7]	MA Yuanzhi, QIN Xiaolin, LING Hongbo, YAN Junjie, ZHANG Guangpeng. Spatio-temporal characteristics and trends of area changes in the small and medium-sized lakes in Xinjiang, China, from 1991 to 2020 [J]. Arid Zone Research, 2024, 41(6): 905-916.
[8]	LIANG Shuanghe, NIU Zuirong, JIA Ling. Analysis of runoff changes and attribution in the main stream of Zuli River in the past 65 years [J]. Arid Zone Research, 2024, 41(6): 928-939.
[9]	TANG Kexin, GUO Jianbin, HE Liang, CHEN Lin, WAN Long. Characteristics of the spatial and temporal evolution of Gross Primary Productivity and its influencing factors in China’s drylands [J]. Arid Zone Research, 2024, 41(6): 964-973.
[10]	ZHANG Haozhe, XUE Yayong, MA Yuanyuan, XUE Guoxuan. Carbon sequestration potential of oasis ecosystem in Xinjiang, China [J]. Arid Zone Research, 2024, 41(6): 998-1009.
[11]	XU Chaojie, DOU Yan, MENG Qilin. Prediction of the standardized precipitation evapotranspiration index in the Xinjiang region using the EMD-GWO-LSTM model [J]. Arid Zone Research, 2024, 41(4): 527-539.
[12]	SI Qi, FAN Haoran, DONG Wenming, LIU Xinping. Landscape ecological risk assessment and prediction for the Yarkant River Basin, Xinjiang, China [J]. Arid Zone Research, 2024, 41(4): 684-696.
[13]	BAO Jiayu, LI Xianglong, HU Qiwen, LI Tao. Spatiotemporal characteristics of carbon emissions from energy consumption and the approach to energy structure adjustment in Xinjiang [J]. Arid Zone Research, 2024, 41(3): 490-498.
[14]	YAO Junqiang. Change in atmospheric and surface water resource in Xinjiang [J]. Arid Zone Research, 2024, 41(2): 181-190.
[15]	ZHANG Jiaqi, LIU Zhao, HAN Zhongqing, WANG Lixia, ZHANG Jinxia, YUE Jiayin, GUAN Zilong. Trend change and prediction of blue-green water in the Jinghe River Basin under climate change [J]. Arid Zone Research, 2024, 41(12): 2045-2055.